Extensive descriptions of the LEAF GUI software can be found in the
accompanying manuscript and in the user manual.

Here
we describe generally the series of steps that a user might take to go from a
cleared leaf image to several potential resulting images and data tables. The
overall process can be broken down in to four major steps: 1) Initial image
cropping. 2) Image thresholding. 3) Binary image cleaning and processing. 4)
Feature extraction.

Step 1. Setting the scale

There are two options to set
the image scale. If the scale is known (in cm/pixel), it can simply be entered
in the text box in the Set Scale panel. Alternatively, if the image contains a
scale bar, the user enters the known scale in cm then clicks on the “Measure
Scale” button. When the cursor is placed over the original image, crosshairs now
appear. The user then clicks on either end of the scale bar, taking care that
the interactive line they are creating, corresponds as closely as possible to
the known scale. When finished, double-clicking on the bar will cause the scale
to be displayed in the Set Scale text box.

Step 2. Cropping the initial image.

We provide two options to
crop the initial image, rectangular or polygonal cropping. Either cropping
method is useful when extraneous features such as scale bars, labels, other
leaves or image noise needs to be removed from the image. The choice between the
two methods depends primarily on the location of the noise in the image.

Step 3. Image segmentation/thresholding.

In computer vision,
segmentation refers to the
process of breaking up a
digital image
into multiple
segments
(sets
of
pixels). The goal of segmentation is to change
the representation of an image into something that is more meaningful and easier
to analyze. We use two different thresholding methods (local and global), used
separately or combined to segment leaf vein images into a binary image where
leaf veins are represented by ones, and non vein regions by zeros. The global
thresholding simply takes a grayscale copy of the original image, where pixel
values range from 0-255, and removes pixels above a certain threshold, for
example, all pixels with a value of 125 or greater. Adaptive thresholding is the
same in principle to global thresholding, but in contrast it accounts for the
fact that images are often unevenly illuminated. To correct for uneven
illumination, thresholding is performed on each pixel within a local window,
where the pixel is subtracted (set to zero) from the image if its value is
greater than some value X from the
mean pixel value within that window: Both
X and the window sizes are interactively set by the user.

Step 4. Binary image cleaning and processing.

Once the image has been
thresholded, a series of steps might be employed to further clean and enhance
vein representation in the binary image. The choice and sequence are specific to
the user requirements. These include removing unwanted contiguous regions below
a certain size cutoff (e.g. 10 pixels), removing the leaf perimeter in single
pixel wide steps, bridging non-connected segments, filling single pixel holes,
removing extraneous spurs (single pixel wide extrusions), filling or removing
user specified polygonal regions, clearing regions overlapping the border, or
removing labeled regions. At any point the user can create a complement of the
binary image (zeros become ones, ones become zeros) and perform all of these
same tasks. There is also an option to create a mask, through the use of a very
high or low threshold, where the leaf (veins and areoles) are entirely white and
the background is black. This step is useful in removing unwanted background
noise following thresholding.

At any point during this process the
user has the option to use one of several visualization options to see how well
the image is being segmented. All of these options relate to one or more aspects
of the statistical algorithms utilized in subsequent steps. This can include the
skeleton of the vein network, a distance transform on either the areoles or
veins, labeling leaf veins or areoles (assigning a numerical identifier to each
contiguous vein or areole region) both of which can indicate how well the
network is connected and consequently how well areoles are delineated.

Step 5. Summary Statistics

There are the four primary
options the user can select within the Summary Statistics Panel to return
descriptive statistics from the leaf image. These are broken down into four
buttons corresponding to the different types of statistics: Area Stats, Vein
Stats, Areole Stats, and a Connectivity Matrix. The output is either an Excel
spreadsheet, or a tab delimited text file based on user preferences.

Area Stats:
returns simply the area and perimeter of the binary image.

Areole Stats:
returns the spatial position, area, convex area, solidity (the ratio of area to
convex area), eccentricity, equivalent diameter, centroid position, mean
distance to the nearest vein and the variance in that measure.

Vein Stats:
are the length, width and spatial position of every edge within the leaf. In
addition the software returns the 2D area occupied by each edge and estimates
for the surface area and volume based on the assumption that each edge is
approximately cylindrical.

Connectivity Matrix:
This button returns a n x 3 matrix (n=number
of edges) showing which labeled nodes (columns 1 and 2) are connected by which
labeled edges (column 3). This can be combined with information in the Vein
Stats output, which contains the dimensions and labels for all edges, in
subsequent analyses.